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Add WebSocket-based RAM output for CanvasNode

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Introduces a WebSocket-based mechanism for CanvasNode to send and receive canvas image and mask data in RAM, enabling fast, diskless data transfer between frontend and backend. Adds a new WebSocketManager utility, updates CanvasIO to support RAM output mode, and modifies CanvasView to send canvas data via WebSocket before prompt execution. The backend (canvas_node.py) is updated to handle WebSocket data storage and retrieval, with improved locking and cleanup logic. This change improves workflow speed and reliability by avoiding unnecessary disk I/O and ensuring up-to-date canvas data is available during node execution.
This commit is contained in:
Dariusz L
2025-06-27 05:28:13 +02:00
parent daf3abeea7
commit be4fae2964
6 changed files with 602 additions and 254 deletions
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293
canvas_node.py
View File

@@ -5,6 +5,8 @@ import numpy as np
import folder_paths
from server import PromptServer
from aiohttp import web
import asyncio
import threading
import os
from tqdm import tqdm
from torchvision import transforms
@@ -91,6 +93,9 @@ class BiRefNet(torch.nn.Module):
class CanvasNode:
_canvas_data_storage = {}
_storage_lock = threading.Lock()
_canvas_cache = {
'image': None,
'mask': None,
@@ -100,9 +105,15 @@ class CanvasNode:
'last_execution_id': None
}
# Simple in-memory storage for canvas data, keyed by prompt_id
# WebSocket-based storage for canvas data per node
_websocket_data = {}
_websocket_listeners = {}
def __init__(self):
super().__init__()
self.flow_id = str(uuid.uuid4())
self.node_id = None # Will be set when node is created
if self.__class__._canvas_cache['persistent_cache']:
self.restore_cache()
@@ -166,14 +177,18 @@ class CanvasNode:
def INPUT_TYPES(cls):
return {
"required": {
"canvas_image": ("STRING", {"default": "canvas_image.png"}),
"trigger": ("INT", {"default": 0, "min": 0, "max": 99999999, "step": 1, "hidden": True}),
"output_switch": ("BOOLEAN", {"default": True}),
"cache_enabled": ("BOOLEAN", {"default": True, "label": "Enable Cache"})
"cache_enabled": ("BOOLEAN", {"default": True, "label": "Enable Cache"}),
"node_id": ("STRING", {"default": "0", "hidden": True}),
},
"optional": {
"input_image": ("IMAGE",),
"input_mask": ("MASK",)
},
"hidden": {
"prompt": ("PROMPT",),
"unique_id": ("UNIQUE_ID",),
}
}
@@ -230,161 +245,72 @@ class CanvasNode:
return None
# Zmienna blokująca równoczesne wykonania
_processing_lock = None
_processing_lock = threading.Lock()
def process_canvas_image(self, canvas_image, trigger, output_switch, cache_enabled, input_image=None,
def process_canvas_image(self, trigger, output_switch, cache_enabled, node_id, prompt=None, unique_id=None, input_image=None,
input_mask=None):
log_info(f"[CanvasNode] 🔍 process_canvas_image wejście – node_id={node_id!r}, unique_id={unique_id!r}, trigger={trigger}, output_switch={output_switch}")
try:
# Sprawdź czy już trwa przetwarzanie
if self.__class__._processing_lock is not None:
log_warn(f"Process already in progress, waiting for completion...")
return () # Zwróć pusty wynik, aby uniknąć równoczesnych przetworzeń
if not self.__class__._processing_lock.acquire(blocking=False):
log_warn(f"Process already in progress for node {node_id}, skipping...")
# Return cached data if available to avoid breaking the flow
return self.get_cached_data()
# Ustaw blokadę
self.__class__._processing_lock = True
log_info(f"Lock acquired. Starting process_canvas_image for node_id: {node_id} (fallback unique_id: {unique_id})")
current_execution = self.get_execution_id()
log_info(f"Starting process_canvas_image - execution ID: {current_execution}, trigger: {trigger}")
log_debug(f"Canvas image filename: {canvas_image}")
log_debug(f"Output switch: {output_switch}, Cache enabled: {cache_enabled}")
log_debug(f"Input image provided: {input_image is not None}")
log_debug(f"Input mask provided: {input_mask is not None}")
# Use node_id as the primary key, as unique_id is proving unreliable
storage_key = node_id
if current_execution != self.__class__._canvas_cache['last_execution_id']:
log_info(f"New execution detected: {current_execution} (previous: {self.__class__._canvas_cache['last_execution_id']})")
processed_image = None
processed_mask = None
self.__class__._canvas_cache['image'] = None
self.__class__._canvas_cache['mask'] = None
self.__class__._canvas_cache['last_execution_id'] = current_execution
with self.__class__._storage_lock:
canvas_data = self.__class__._canvas_data_storage.pop(storage_key, None)
if canvas_data:
log_info(f"Canvas data found for node {storage_key} from WebSocket")
if canvas_data.get('image'):
image_data = canvas_data['image'].split(',')[1]
image_bytes = base64.b64decode(image_data)
pil_image = Image.open(io.BytesIO(image_bytes)).convert('RGB')
image_array = np.array(pil_image).astype(np.float32) / 255.0
processed_image = torch.from_numpy(image_array)[None,]
log_debug(f"Image loaded from WebSocket, shape: {processed_image.shape}")
if canvas_data.get('mask'):
mask_data = canvas_data['mask'].split(',')[1]
mask_bytes = base64.b64decode(mask_data)
pil_mask = Image.open(io.BytesIO(mask_bytes)).convert('L')
mask_array = np.array(pil_mask).astype(np.float32) / 255.0
processed_mask = torch.from_numpy(mask_array)[None,]
log_debug(f"Mask loaded from WebSocket, shape: {processed_mask.shape}")
else:
log_debug(f"Same execution ID, using cached data")
log_warn(f"No canvas data found for node {storage_key} in WebSocket cache, using fallbacks.")
if input_image is not None:
log_info("Input image received, converting to PIL Image...")
if isinstance(input_image, torch.Tensor):
if input_image.dim() == 4:
input_image = input_image.squeeze(0) # 移除batch维度
if input_image.shape[0] == 3: # 如果是[C, H, W]格式
input_image = input_image.permute(1, 2, 0)
image_array = (input_image.cpu().numpy() * 255).astype(np.uint8)
if len(image_array.shape) == 2: # 如果是灰度图
image_array = np.stack([image_array] * 3, axis=-1)
elif len(image_array.shape) == 3 and image_array.shape[-1] != 3:
image_array = np.transpose(image_array, (1, 2, 0))
try:
pil_image = Image.fromarray(image_array, 'RGB')
log_debug("Successfully converted to PIL Image")
self.__class__._canvas_cache['image'] = pil_image
log_debug(f"Image stored in cache with size: {pil_image.size}")
except Exception as e:
log_error(f"Error converting to PIL Image: {str(e)}")
log_debug(f"Array shape: {image_array.shape}, dtype: {image_array.dtype}")
raise
log_info("Using provided input_image as fallback")
processed_image = input_image
if input_mask is not None:
log_info("Input mask received, converting to PIL Image...")
if isinstance(input_mask, torch.Tensor):
if input_mask.dim() == 4:
input_mask = input_mask.squeeze(0)
if input_mask.dim() == 3 and input_mask.shape[0] == 1:
input_mask = input_mask.squeeze(0)
log_info("Using provided input_mask as fallback")
processed_mask = input_mask
mask_array = (input_mask.cpu().numpy() * 255).astype(np.uint8)
pil_mask = Image.fromarray(mask_array, 'L')
log_debug("Successfully converted mask to PIL Image")
self.__class__._canvas_cache['mask'] = pil_mask
log_debug(f"Mask stored in cache with size: {pil_mask.size}")
# Fallback to default tensors if nothing is loaded
if processed_image is None:
log_warn(f"Processed image is still None, creating default blank image.")
processed_image = torch.zeros((1, 512, 512, 3), dtype=torch.float32)
if processed_mask is None:
log_warn(f"Processed mask is still None, creating default blank mask.")
processed_mask = torch.zeros((1, 512, 512), dtype=torch.float32)
self.__class__._canvas_cache['cache_enabled'] = cache_enabled
try:
# Wczytaj obraz bez maski
image_without_mask_name = canvas_image.replace('.png', '_without_mask.png')
path_image_without_mask = folder_paths.get_annotated_filepath(image_without_mask_name)
log_debug(f"Canvas image name: {canvas_image}")
log_debug(f"Looking for image without mask: {image_without_mask_name}")
log_debug(f"Full path: {path_image_without_mask}")
# Sprawdź czy plik istnieje
if not os.path.exists(path_image_without_mask):
log_warn(f"Image without mask not found at: {path_image_without_mask}")
# Spróbuj znaleźć plik w katalogu input
input_dir = folder_paths.get_input_directory()
alternative_path = os.path.join(input_dir, image_without_mask_name)
log_debug(f"Trying alternative path: {alternative_path}")
if os.path.exists(alternative_path):
path_image_without_mask = alternative_path
log_info(f"Found image at alternative path: {alternative_path}")
else:
raise FileNotFoundError(f"Image file not found: {image_without_mask_name}")
i = Image.open(path_image_without_mask)
i = ImageOps.exif_transpose(i)
if i.mode not in ['RGB', 'RGBA']:
i = i.convert('RGB')
image = np.array(i).astype(np.float32) / 255.0
if i.mode == 'RGBA':
rgb = image[..., :3]
alpha = image[..., 3:]
image = rgb * alpha + (1 - alpha) * 0.5
processed_image = torch.from_numpy(image)[None,]
log_debug(f"Successfully loaded image without mask, shape: {processed_image.shape}")
except Exception as e:
log_error(f"Error loading image without mask: {str(e)}")
processed_image = torch.ones((1, 512, 512, 3), dtype=torch.float32)
log_debug(f"Using default image, shape: {processed_image.shape}")
try:
# Wczytaj maskę
path_image = folder_paths.get_annotated_filepath(canvas_image)
path_mask = path_image.replace('.png', '_mask.png')
log_debug(f"Canvas image path: {path_image}")
log_debug(f"Looking for mask at: {path_mask}")
# Sprawdź czy plik maski istnieje
if not os.path.exists(path_mask):
log_warn(f"Mask not found at: {path_mask}")
# Spróbuj znaleźć plik w katalogu input
input_dir = folder_paths.get_input_directory()
mask_name = canvas_image.replace('.png', '_mask.png')
alternative_mask_path = os.path.join(input_dir, mask_name)
log_debug(f"Trying alternative mask path: {alternative_mask_path}")
if os.path.exists(alternative_mask_path):
path_mask = alternative_mask_path
log_info(f"Found mask at alternative path: {alternative_mask_path}")
if os.path.exists(path_mask):
log_debug(f"Mask file exists, loading...")
mask = Image.open(path_mask).convert('L')
mask = np.array(mask).astype(np.float32) / 255.0
processed_mask = torch.from_numpy(mask)[None,]
log_debug(f"Successfully loaded mask, shape: {processed_mask.shape}")
else:
log_debug(f"Mask file does not exist, creating default mask")
processed_mask = torch.ones((1, processed_image.shape[1], processed_image.shape[2]),
dtype=torch.float32)
log_debug(f"Default mask created, shape: {processed_mask.shape}")
except Exception as e:
log_error(f"Error loading mask: {str(e)}")
processed_mask = torch.ones((1, processed_image.shape[1], processed_image.shape[2]),
dtype=torch.float32)
log_debug(f"Fallback mask created, shape: {processed_mask.shape}")
if not output_switch:
log_debug(f"Output switch is OFF, returning empty tuple")
return ()
return (None, None)
log_debug(f"About to return output - Image shape: {processed_image.shape}, Mask shape: {processed_mask.shape}")
log_debug(f"Image tensor info - dtype: {processed_image.dtype}, device: {processed_image.device}")
log_debug(f"Mask tensor info - dtype: {processed_mask.dtype}, device: {processed_mask.device}")
self.update_persistent_cache()
@@ -393,12 +319,13 @@ class CanvasNode:
except Exception as e:
log_exception(f"Error in process_canvas_image: {str(e)}")
return ()
return (None, None)
finally:
# Zwolnij blokadę
self.__class__._processing_lock = None
log_debug(f"Process completed, lock released")
if self.__class__._processing_lock.locked():
self.__class__._processing_lock.release()
log_debug(f"Process completed for node {node_id}, lock released")
def get_cached_data(self):
return {
@@ -440,8 +367,80 @@ class CanvasNode:
return cls._canvas_cache['data_flow_status'].get(flow_id)
return cls._canvas_cache['data_flow_status']
@classmethod
def _cleanup_old_websocket_data(cls):
"""Clean up old WebSocket data from invalid nodes or data older than 5 minutes"""
try:
current_time = time.time()
cleanup_threshold = 300 # 5 minutes
nodes_to_remove = []
for node_id, data in cls._websocket_data.items():
# Remove invalid node IDs
if node_id < 0:
nodes_to_remove.append(node_id)
continue
# Remove old data
if current_time - data.get('timestamp', 0) > cleanup_threshold:
nodes_to_remove.append(node_id)
continue
for node_id in nodes_to_remove:
del cls._websocket_data[node_id]
log_debug(f"Cleaned up old WebSocket data for node {node_id}")
if nodes_to_remove:
log_info(f"Cleaned up {len(nodes_to_remove)} old WebSocket entries")
except Exception as e:
log_error(f"Error during WebSocket cleanup: {str(e)}")
@classmethod
def setup_routes(cls):
@PromptServer.instance.routes.get("/layerforge/canvas_ws")
async def handle_canvas_websocket(request):
ws = web.WebSocketResponse()
await ws.prepare(request)
async for msg in ws:
if msg.type == web.WSMsgType.TEXT:
try:
data = msg.json()
node_id = data.get('nodeId')
if not node_id:
await ws.send_json({'status': 'error', 'message': 'nodeId is required'})
continue
image_data = data.get('image')
mask_data = data.get('mask')
with cls._storage_lock:
cls._canvas_data_storage[node_id] = {
'image': image_data,
'mask': mask_data,
'timestamp': time.time()
}
log_info(f"Received canvas data for node {node_id} via WebSocket")
# Send acknowledgment back to the client
ack_payload = {
'type': 'ack',
'nodeId': node_id,
'status': 'success'
}
await ws.send_json(ack_payload)
log_debug(f"Sent ACK for node {node_id}")
except Exception as e:
log_error(f"Error processing WebSocket message: {e}")
await ws.send_json({'status': 'error', 'message': str(e)})
elif msg.type == web.WSMsgType.ERROR:
log_error(f"WebSocket connection closed with exception {ws.exception()}")
log_info("WebSocket connection closed")
return ws
@PromptServer.instance.routes.get("/ycnode/get_canvas_data/{node_id}")
async def get_canvas_data(request):
try:
@@ -811,3 +810,15 @@ def convert_tensor_to_base64(tensor, alpha_mask=None, original_alpha=None):
log_error(f"Error in convert_tensor_to_base64: {str(e)}")
log_debug(f"Tensor shape: {tensor.shape}, dtype: {tensor.dtype}")
raise
# Setup original API routes when module is loaded
CanvasNode.setup_routes()
NODE_CLASS_MAPPINGS = {
"CanvasNode": CanvasNode
}
NODE_DISPLAY_NAME_MAPPINGS = {
"CanvasNode": "LayerForge"
}

3
js/Canvas.js
View File

@@ -266,9 +266,6 @@ export class Canvas {
async saveToServer(fileName) {
return this.canvasIO.saveToServer(fileName);
}
async getFlattenedCanvasAsBlob() {
return this.canvasLayers.getFlattenedCanvasAsBlob();

157
js/CanvasIO.js
View File

@@ -1,5 +1,6 @@
import {createCanvas} from "./utils/CommonUtils.js";
import {createModuleLogger} from "./utils/LoggerUtils.js";
import {webSocketManager} from "./utils/WebSocketManager.js";
const log = createModuleLogger('CanvasIO');
@@ -9,7 +10,8 @@ export class CanvasIO {
this._saveInProgress = null;
}
async saveToServer(fileName) {
async saveToServer(fileName, outputMode = 'disk') {
if (outputMode === 'disk') {
if (!window.canvasSaveStates) {
window.canvasSaveStates = new Map();
}
@@ -21,23 +23,25 @@ export class CanvasIO {
return this._saveInProgress || window.canvasSaveStates.get(saveKey);
}
log.info(`Starting saveToServer with fileName: ${fileName} for node: ${nodeId}`);
log.debug(`Canvas dimensions: ${this.canvas.width}x${this.canvas.height}`);
log.debug(`Number of layers: ${this.canvas.layers.length}`);
this._saveInProgress = this._performSave(fileName);
log.info(`Starting saveToServer (disk) with fileName: ${fileName} for node: ${nodeId}`);
this._saveInProgress = this._performSave(fileName, outputMode);
window.canvasSaveStates.set(saveKey, this._saveInProgress);
try {
const result = await this._saveInProgress;
return result;
return await this._saveInProgress;
} finally {
this._saveInProgress = null;
window.canvasSaveStates.delete(saveKey);
log.debug(`Save completed for node ${nodeId}, lock released`);
}
} else {
// For RAM mode, we don't need the lock/state management as it's synchronous
log.info(`Starting saveToServer (RAM) for node: ${this.canvas.node.id}`);
return this._performSave(fileName, outputMode);
}
}
async _performSave(fileName) {
async _performSave(fileName, outputMode) {
if (this.canvas.layers.length === 0) {
log.warn(`Node ${this.canvas.node.id} has no layers, creating empty canvas`);
return Promise.resolve(true);
@@ -152,6 +156,15 @@ export class CanvasIO {
maskCtx.globalCompositeOperation = 'source-over';
maskCtx.drawImage(tempMaskCanvas, 0, 0);
}
if (outputMode === 'ram') {
const imageData = tempCanvas.toDataURL('image/png');
const maskData = maskCanvas.toDataURL('image/png');
log.info("Returning image and mask data as base64 for RAM mode.");
resolve({ image: imageData, mask: maskData });
return;
}
// --- Disk Mode (original logic) ---
const fileNameWithoutMask = fileName.replace('.png', '_without_mask.png');
log.info(`Saving image without mask as: ${fileNameWithoutMask}`);
@@ -204,7 +217,9 @@ export class CanvasIO {
if (maskResp.status === 200) {
const data = await resp.json();
if (this.canvas.widget) {
this.canvas.widget.value = fileName;
}
log.info(`All files saved successfully, widget value set to: ${fileName}`);
resolve(true);
} else {
@@ -228,6 +243,132 @@ export class CanvasIO {
});
}
async _renderOutputData() {
return new Promise((resolve) => {
const { canvas: tempCanvas, ctx: tempCtx } = createCanvas(this.canvas.width, this.canvas.height);
const { canvas: maskCanvas, ctx: maskCtx } = createCanvas(this.canvas.width, this.canvas.height);
// This logic is mostly mirrored from _performSave to ensure consistency
tempCtx.fillStyle = '#ffffff';
tempCtx.fillRect(0, 0, this.canvas.width, this.canvas.height);
const visibilityCanvas = document.createElement('canvas');
visibilityCanvas.width = this.canvas.width;
visibilityCanvas.height = this.canvas.height;
const visibilityCtx = visibilityCanvas.getContext('2d', { alpha: true });
maskCtx.fillStyle = '#ffffff'; // Start with a white mask (nothing masked)
maskCtx.fillRect(0, 0, this.canvas.width, this.canvas.height);
const sortedLayers = this.canvas.layers.sort((a, b) => a.zIndex - b.zIndex);
sortedLayers.forEach((layer) => {
// Render layer to main canvas
tempCtx.save();
tempCtx.globalCompositeOperation = layer.blendMode || 'normal';
tempCtx.globalAlpha = layer.opacity !== undefined ? layer.opacity : 1;
tempCtx.translate(layer.x + layer.width / 2, layer.y + layer.height / 2);
tempCtx.rotate(layer.rotation * Math.PI / 180);
tempCtx.drawImage(layer.image, -layer.width / 2, -layer.height / 2, layer.width, layer.height);
tempCtx.restore();
// Render layer to visibility canvas for the mask
visibilityCtx.save();
visibilityCtx.translate(layer.x + layer.width / 2, layer.y + layer.height / 2);
visibilityCtx.rotate(layer.rotation * Math.PI / 180);
visibilityCtx.drawImage(layer.image, -layer.width / 2, -layer.height / 2, layer.width, layer.height);
visibilityCtx.restore();
});
// Create layer visibility mask
const visibilityData = visibilityCtx.getImageData(0, 0, this.canvas.width, this.canvas.height);
const maskData = maskCtx.getImageData(0, 0, this.canvas.width, this.canvas.height);
for (let i = 0; i < visibilityData.data.length; i += 4) {
const alpha = visibilityData.data[i + 3];
const maskValue = 255 - alpha; // Invert alpha to create the mask
maskData.data[i] = maskData.data[i + 1] = maskData.data[i + 2] = maskValue;
maskData.data[i + 3] = 255; // Solid mask
}
maskCtx.putImageData(maskData, 0, 0);
// Composite the tool mask on top
const toolMaskCanvas = this.canvas.maskTool.getMask();
if (toolMaskCanvas) {
// Create a temp canvas for processing the mask
const tempMaskCanvas = document.createElement('canvas');
tempMaskCanvas.width = this.canvas.width;
tempMaskCanvas.height = this.canvas.height;
const tempMaskCtx = tempMaskCanvas.getContext('2d');
// Clear the canvas
tempMaskCtx.clearRect(0, 0, tempMaskCanvas.width, tempMaskCanvas.height);
// Calculate the correct position to extract the mask
const maskX = this.canvas.maskTool.x;
const maskY = this.canvas.maskTool.y;
log.debug(`[renderOutputData] Extracting mask from world position (${maskX}, ${maskY})`);
const sourceX = Math.max(0, -maskX);
const sourceY = Math.max(0, -maskY);
const destX = Math.max(0, maskX);
const destY = Math.max(0, maskY);
const copyWidth = Math.min(toolMaskCanvas.width - sourceX, this.canvas.width - destX);
const copyHeight = Math.min(toolMaskCanvas.height - sourceY, this.canvas.height - destY);
if (copyWidth > 0 && copyHeight > 0) {
tempMaskCtx.drawImage(
toolMaskCanvas,
sourceX, sourceY, copyWidth, copyHeight,
destX, destY, copyWidth, copyHeight
);
}
// Convert the brush mask (white with alpha) to a solid white mask on black background.
const tempMaskData = tempMaskCtx.getImageData(0, 0, this.canvas.width, this.canvas.height);
for (let i = 0; i < tempMaskData.data.length; i += 4) {
const alpha = tempMaskData.data[i + 3];
// The painted area (alpha > 0) should become white (255).
tempMaskData.data[i] = tempMaskData.data[i+1] = tempMaskData.data[i+2] = alpha;
tempMaskData.data[i + 3] = 255; // Solid alpha
}
tempMaskCtx.putImageData(tempMaskData, 0, 0);
// Use 'screen' blending mode. This correctly adds the white brush mask
// to the existing layer visibility mask. (white + anything = white)
maskCtx.globalCompositeOperation = 'screen';
maskCtx.drawImage(tempMaskCanvas, 0, 0);
}
const imageDataUrl = tempCanvas.toDataURL('image/png');
const maskDataUrl = maskCanvas.toDataURL('image/png');
resolve({ image: imageDataUrl, mask: maskDataUrl });
});
}
async sendDataViaWebSocket(nodeId) {
log.info(`Preparing to send data for node ${nodeId} via WebSocket.`);
const { image, mask } = await this._renderOutputData();
try {
log.info(`Sending data for node ${nodeId}...`);
await webSocketManager.sendMessage({
type: 'canvas_data',
nodeId: String(nodeId),
image: image,
mask: mask,
}, true); // `true` requires an acknowledgment
log.info(`Data for node ${nodeId} has been sent and acknowledged by the server.`);
return true;
} catch (error) {
log.error(`Failed to send data for node ${nodeId}:`, error);
// We can alert the user here or handle it silently.
// For now, let's throw to make it clear the process failed.
throw new Error(`Failed to get confirmation from server for node ${nodeId}. The workflow might not have the latest canvas data.`);
}
}
async addInputToCanvas(inputImage, inputMask) {
try {
log.debug("Adding input to canvas:", {inputImage});

172
js/CanvasView.js
View File

@@ -377,8 +377,7 @@ async function createCanvasWidget(node, widget, app) {
const img = new Image();
img.onload = async () => {
canvas.addLayer(img);
await saveWithFallback(widget.value);
app.graph.runStep();
await updateOutput();
};
img.src = event.target.result;
};
@@ -392,8 +391,7 @@ async function createCanvasWidget(node, widget, app) {
textContent: "Import Input",
onclick: async () => {
if (await canvas.importLatestImage()) {
await saveWithFallback(widget.value);
app.graph.runStep();
await updateOutput();
}
}
}),
@@ -574,8 +572,7 @@ async function createCanvasWidget(node, widget, app) {
canvas.updateSelection([newLayer]);
canvas.render();
canvas.saveState();
await saveWithFallback(widget.value);
app.graph.runStep();
await updateOutput();
} catch (error) {
log.error("Matting error:", error);
alert(`Error during matting process: ${error.message}`);
@@ -745,7 +742,8 @@ async function createCanvasWidget(node, widget, app) {
const triggerWidget = node.widgets.find(w => w.name === "trigger");
const updateOutput = async () => {
await saveWithFallback(widget.value);
// Only increment trigger and run step - don't save to disk here
// Saving to disk will happen during execution_start event
triggerWidget.value = (triggerWidget.value + 1) % 99999999;
app.graph.runStep();
};
@@ -790,8 +788,9 @@ async function createCanvasWidget(node, widget, app) {
canvas.render();
};
canvas.canvas.addEventListener('mouseup', updateOutput);
canvas.canvas.addEventListener('mouseleave', updateOutput);
// Remove automatic saving on mouse events - only save during execution
// canvas.canvas.addEventListener('mouseup', updateOutput);
// canvas.canvas.addEventListener('mouseleave', updateOutput);
const mainContainer = $el("div.painterMainContainer", {
@@ -922,66 +921,8 @@ async function createCanvasWidget(node, widget, app) {
if (!window.canvasExecutionStates) {
window.canvasExecutionStates = new Map();
}
const saveWithFallback = async (fileName) => {
try {
const uniqueFileName = generateUniqueFileName(fileName, node.id);
log.debug(`Attempting to save with unique name: ${uniqueFileName}`);
return await canvas.saveToServer(uniqueFileName);
} catch (error) {
log.warn(`Failed to save with unique name, falling back to original: ${fileName}`, error);
return await canvas.saveToServer(fileName);
}
};
api.addEventListener("execution_start", async (event) => {
const executionData = event.detail || {};
const currentPromptId = executionData.prompt_id;
log.info(`Execution start event for node ${node.id}, prompt_id: ${currentPromptId}`);
log.debug(`Widget value: ${widget.value}`);
log.debug(`Node inputs: ${node.inputs?.length || 0}`);
log.debug(`Canvas layers count: ${canvas.layers.length}`);
if (window.canvasExecutionStates.get(node.id)) {
log.warn(`Execution already in progress for node ${node.id}, skipping...`);
return;
}
window.canvasExecutionStates.set(node.id, true);
try {
if (canvas.layers.length === 0) {
log.warn(`Node ${node.id} has no layers, skipping save to server`);
} else {
await saveWithFallback(widget.value);
log.info(`Canvas saved to server for node ${node.id}`);
}
if (node.inputs[0]?.link) {
const linkId = node.inputs[0].link;
const inputData = app.nodeOutputs[linkId];
log.debug(`Input link ${linkId} has data: ${!!inputData}`);
if (inputData) {
imageCache.set(linkId, inputData);
log.debug(`Input data cached for link ${linkId}`);
}
} else {
log.debug(`No input link found`);
}
} catch (error) {
log.error(`Error during execution for node ${node.id}:`, error);
} finally {
window.canvasExecutionStates.set(node.id, false);
log.debug(`Execution completed for node ${node.id}, flag released`);
}
});
const originalSaveToServer = canvas.saveToServer;
canvas.saveToServer = async function (fileName) {
log.debug(`saveToServer called with fileName: ${fileName}`);
log.debug(`Current execution context - node ID: ${node.id}`);
const result = await originalSaveToServer.call(this, fileName);
log.debug(`saveToServer completed, result: ${result}`);
return result;
};
node.canvasWidget = canvas;
@@ -996,30 +937,111 @@ async function createCanvasWidget(node, widget, app) {
}
const canvasNodeInstances = new Map();
app.registerExtension({
name: "Comfy.CanvasNode",
init() {
// Monkey-patch the queuePrompt function to send canvas data via WebSocket before sending the prompt
const originalQueuePrompt = app.queuePrompt;
app.queuePrompt = async function(number, prompt) {
log.info("Preparing to queue prompt...");
if (canvasNodeInstances.size > 0) {
log.info(`Found ${canvasNodeInstances.size} CanvasNode(s). Sending data via WebSocket...`);
const sendPromises = [];
for (const [nodeId, canvasWidget] of canvasNodeInstances.entries()) {
// Ensure the node still exists on the graph before sending data
if (app.graph.getNodeById(nodeId) && canvasWidget.canvas && canvasWidget.canvas.canvasIO) {
log.debug(`Sending data for canvas node ${nodeId}`);
// This now returns a promise that resolves upon server ACK
sendPromises.push(canvasWidget.canvas.canvasIO.sendDataViaWebSocket(nodeId));
} else {
// If node doesn't exist, it might have been deleted, so we can clean up the map
log.warn(`Node ${nodeId} not found in graph, removing from instances map.`);
canvasNodeInstances.delete(nodeId);
}
}
try {
// Wait for all WebSocket messages to be acknowledged
await Promise.all(sendPromises);
log.info("All canvas data has been sent and acknowledged by the server.");
} catch (error) {
log.error("Failed to send canvas data for one or more nodes. Aborting prompt.", error);
// IMPORTANT: Stop the prompt from queueing if data transfer fails.
// You might want to show a user-facing error here.
alert(`CanvasNode Error: ${error.message}`);
return; // Stop execution
}
}
log.info("All pre-prompt tasks complete. Proceeding with original queuePrompt.");
// Proceed with the original queuePrompt logic
return originalQueuePrompt.apply(this, arguments);
};
},
async beforeRegisterNodeDef(nodeType, nodeData, app) {
if (nodeType.comfyClass === "CanvasNode") {
const onNodeCreated = nodeType.prototype.onNodeCreated;
nodeType.prototype.onNodeCreated = async function () {
log.info("CanvasNode created, ID:", this.id);
nodeType.prototype.onNodeCreated = function () {
log.debug("CanvasNode onNodeCreated: Base widget setup.");
// Call original onNodeCreated to ensure widgets are created
const r = onNodeCreated?.apply(this, arguments);
const widget = this.widgets.find(w => w.name === "canvas_image");
log.debug("Found canvas_image widget:", widget);
await createCanvasWidget(this, widget, app);
// The main initialization is moved to onAdded
return r;
};
// onAdded is the most reliable callback for when a node is fully added to the graph and has an ID
nodeType.prototype.onAdded = async function() {
log.info(`CanvasNode onAdded, ID: ${this.id}`);
log.debug(`Available widgets in onAdded:`, this.widgets.map(w => w.name));
// Prevent re-initialization if the widget already exists
if (this.canvasWidget) {
log.warn(`CanvasNode ${this.id} already initialized. Skipping onAdded setup.`);
return;
}
// Now that we are in onAdded, this.id is guaranteed to be correct.
// Set the hidden node_id widget's value for backend communication.
const nodeIdWidget = this.widgets.find(w => w.name === "node_id");
if (nodeIdWidget) {
nodeIdWidget.value = String(this.id);
log.debug(`Set hidden node_id widget to: ${nodeIdWidget.value}`);
} else {
log.error("Could not find the hidden node_id widget!");
}
// Create the main canvas widget and register it in our global map
// We pass `null` for the widget parameter as we are not using a pre-defined widget.
const canvasWidget = await createCanvasWidget(this, null, app);
canvasNodeInstances.set(this.id, canvasWidget);
log.info(`Registered CanvasNode instance for ID: ${this.id}`);
};
const onRemoved = nodeType.prototype.onRemoved;
nodeType.prototype.onRemoved = function () {
log.info(`Cleaning up canvas node ${this.id}`);
// Clean up from our instance map
canvasNodeInstances.delete(this.id);
log.info(`Deregistered CanvasNode instance for ID: ${this.id}`);
// Clean up execution state
if (window.canvasExecutionStates) {
window.canvasExecutionStates.delete(this.id);
}
const tooltip = document.getElementById(`painter-help-tooltip-${this.id}`);
if (tooltip) {
tooltip.remove();
}
const backdrop = document.querySelector('.painter-modal-backdrop');
if (backdrop && backdrop.contains(this.canvasWidget.canvas)) {
if (backdrop && backdrop.contains(this.canvasWidget?.canvas)) {
document.body.removeChild(backdrop);
}

27
js/utils/CommonUtils.js
View File

@@ -125,12 +125,29 @@ export function cloneLayers(layers) {
* @returns {string} Sygnatura JSON
*/
export function getStateSignature(layers) {
return JSON.stringify(layers.map(layer => {
const sig = {...layer};
if (sig.imageId) {
sig.imageId = sig.imageId;
return JSON.stringify(layers.map((layer, index) => {
const sig = {
index: index,
x: Math.round(layer.x * 100) / 100, // Round to avoid floating point precision issues
y: Math.round(layer.y * 100) / 100,
width: Math.round(layer.width * 100) / 100,
height: Math.round(layer.height * 100) / 100,
rotation: Math.round((layer.rotation || 0) * 100) / 100,
zIndex: layer.zIndex,
blendMode: layer.blendMode || 'normal',
opacity: layer.opacity !== undefined ? Math.round(layer.opacity * 100) / 100 : 1
};
// Include imageId if available
if (layer.imageId) {
sig.imageId = layer.imageId;
}
delete sig.image;
// Include image src as fallback identifier
if (layer.image && layer.image.src) {
sig.imageSrc = layer.image.src.substring(0, 100); // First 100 chars to avoid huge signatures
}
return sig;
}));
}

160
js/utils/WebSocketManager.js Normal file
View File

@@ -0,0 +1,160 @@
import {createModuleLogger} from "./LoggerUtils.js";
const log = createModuleLogger('WebSocketManager');
class WebSocketManager {
constructor(url) {
this.url = url;
this.socket = null;
this.messageQueue = [];
this.isConnecting = false;
this.reconnectAttempts = 0;
this.maxReconnectAttempts = 10;
this.reconnectInterval = 5000; // 5 seconds
this.ackCallbacks = new Map(); // Store callbacks for messages awaiting ACK
this.messageIdCounter = 0;
this.connect();
}
connect() {
if (this.socket && this.socket.readyState === WebSocket.OPEN) {
log.debug("WebSocket is already open.");
return;
}
if (this.isConnecting) {
log.debug("Connection attempt already in progress.");
return;
}
this.isConnecting = true;
log.info(`Connecting to WebSocket at ${this.url}...`);
try {
this.socket = new WebSocket(this.url);
this.socket.onopen = () => {
this.isConnecting = false;
this.reconnectAttempts = 0;
log.info("WebSocket connection established.");
this.flushMessageQueue();
};
this.socket.onmessage = (event) => {
try {
const data = JSON.parse(event.data);
log.debug("Received message:", data);
if (data.type === 'ack' && data.nodeId) {
const callback = this.ackCallbacks.get(data.nodeId);
if (callback) {
log.debug(`ACK received for nodeId: ${data.nodeId}, resolving promise.`);
callback.resolve(data);
this.ackCallbacks.delete(data.nodeId);
}
}
// Handle other incoming messages if needed
} catch (error) {
log.error("Error parsing incoming WebSocket message:", error);
}
};
this.socket.onclose = (event) => {
this.isConnecting = false;
if (event.wasClean) {
log.info(`WebSocket closed cleanly, code=${event.code}, reason=${event.reason}`);
} else {
log.warn("WebSocket connection died. Attempting to reconnect...");
this.handleReconnect();
}
};
this.socket.onerror = (error) => {
this.isConnecting = false;
log.error("WebSocket error:", error);
// The onclose event will be fired next, which will handle reconnection.
};
} catch (error) {
this.isConnecting = false;
log.error("Failed to create WebSocket connection:", error);
this.handleReconnect();
}
}
handleReconnect() {
if (this.reconnectAttempts < this.maxReconnectAttempts) {
this.reconnectAttempts++;
log.info(`Reconnect attempt ${this.reconnectAttempts}/${this.maxReconnectAttempts}...`);
setTimeout(() => this.connect(), this.reconnectInterval);
} else {
log.error("Max reconnect attempts reached. Giving up.");
}
}
sendMessage(data, requiresAck = false) {
return new Promise((resolve, reject) => {
const nodeId = data.nodeId;
if (requiresAck && !nodeId) {
return reject(new Error("A nodeId is required for messages that need acknowledgment."));
}
const message = JSON.stringify(data);
if (this.socket && this.socket.readyState === WebSocket.OPEN) {
this.socket.send(message);
log.debug("Sent message:", data);
if (requiresAck) {
log.debug(`Message for nodeId ${nodeId} requires ACK. Setting up callback.`);
// Set a timeout for the ACK
const timeout = setTimeout(() => {
this.ackCallbacks.delete(nodeId);
reject(new Error(`ACK timeout for nodeId ${nodeId}`));
log.warn(`ACK timeout for nodeId ${nodeId}.`);
}, 10000); // 10-second timeout
this.ackCallbacks.set(nodeId, {
resolve: (responseData) => {
clearTimeout(timeout);
resolve(responseData);
},
reject: (error) => {
clearTimeout(timeout);
reject(error);
}
});
} else {
resolve(); // Resolve immediately if no ACK is needed
}
} else {
log.warn("WebSocket not open. Queuing message.");
// Note: The current queueing doesn't support ACK promises well.
// For simplicity, we'll focus on the connected case.
// A more robust implementation would wrap the queued message in a function.
this.messageQueue.push(message);
if (!this.isConnecting) {
this.connect();
}
// For now, we reject if not connected and ACK is required.
if (requiresAck) {
reject(new Error("Cannot send message with ACK required while disconnected."));
}
}
});
}
flushMessageQueue() {
log.debug(`Flushing ${this.messageQueue.length} queued messages.`);
// Note: This simple flush doesn't handle ACKs for queued messages.
// This should be acceptable as data is sent right before queueing a prompt,
// at which point the socket should ideally be connected.
while (this.messageQueue.length > 0) {
const message = this.messageQueue.shift();
this.socket.send(message);
}
}
}
// Create a singleton instance of the WebSocketManager
const wsUrl = `ws://${window.location.host}/layerforge/canvas_ws`;
export const webSocketManager = new WebSocketManager(wsUrl);